The proposed research project will explore the parallelization of algorithms used in molecular biology, focusing primarily on algorithms for 10 genetic sequence analysis and 2) genetic linkage analysis. A key aspect of the proposal involves the use of the machine-independent parallel programming language Linda. Genetic Sequence Analysis: We propose to continue work currently in progress to explore design issues in parallelizing algorithms for genetic sequence analysis. This research will focus on 1) the intrinsic parallelizing of an algorithm, and 2) the "parallel database search" and "load balancing" issues involved in running an algorithm simultaneously on many sets of input. Genetic Linkage Analysis: Using the machine-independent parallel programming language Linda, we will explore how the complex algorithm for performing genetic linkage analysis is best parallelized, and will test and refine the parallelized version using data from the laboratory of Dr. Kenneth Kidd of the Yale Department of Human Genetics and the Human Gene Mapping Library in New Haven. An Online Library for Use by Molecular Biology Researchers: As parallelized versions of the algorithms are developed, they will be incorporated into an online library whose use by molecular biology researchers at Yale and elsewhere will be encouraged. This online use of the parallelized algorithms will let us obtain concrete feedback to help guide our efforts.